Rapid Command Selection on Multi-Touch Tablets with Single-Handed HandMark Menus
Bibliographic record
Abstract
Fast command selection is important for touch devices, but there are few techniques that allow accelerated selection while still providing a large command set. HandMark menus [25] propose the use of the hands as landmarks for fast memory-based selection. However, the original HandMark menus rely on bimanual operation, and earlier studies provided only limited evidence for the value of hand-based landmarks as a reference frame for spatial memory. In this paper we address these limitations. We introduce adapted HandMark menus that can be operated with one hand (while the other hand holds the tablet); the new version changes bimanual selection operations into sequential actions with one hand. We carried out three studies of these HandMark menus. The first study showed that the adapted menus still allowed fast performance and the development of spatial memory, even with one-handed use. The second study focused on the value of hands as landmarks, by comparing HandMark menus against a hidden popup menu. This study showed that using the hand as a reference frame significantly improved performance, and was strongly preferred by participants. Our work extends HandMark menus and shows that they are an effective selection method for tablets, and provides new evidence about the value of the hands as a spatial landmark for interaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".